Method and device for evaluating sweet spots of remaining gas in tight sand-shale gas reservoirs

Abstract

The application relates to a method for evaluating a remaining gas sweet spot area of a compact sandstone conglomerate gas reservoir, and belongs to the technical field of oilfield development. The method comprises the following steps: using structures, sedimentary facies and a reservoir model to preferably select a static remaining gas potential area; using a numerical model current pressure and remaining gas abundance to determine a current remaining gas potential area; combining the dynamic and static conditions to preferably select a current remaining gas sweet spot area; carrying out numerical model scheme prediction, using cumulative production, daily production, pressure and remaining gas abundance to determine an optimal scheme; determining a future remaining gas potential area on a pressure and remaining gas abundance diagram predicted by the scheme; combining the dynamic and static conditions to preferably select a future remaining gas sweet spot area; taking the intersection of the current and future remaining gas sweet spot areas to determine a final remaining gas sweet spot area; and deploying a virtual well in the sweet spot area to verify the final remaining gas sweet spot area. The application defines a remaining gas sweet spot area selection method, determines a final remaining gas sweet spot area, and realizes systematic, accurate and rapid selection of a remaining gas sweet spot area of a compact sandstone conglomerate gas reservoir.

Description

Technical Field

[0001] This invention relates to the field of complex oil and gas reservoir development technology, and in particular to a method and apparatus for evaluating the remaining sweet spot of tight sandstone and conglomerate gas reservoirs. Background Technology

[0002] As the exploitation of tight sandstone and conglomerate gas reservoirs intensifies, recoverable reserves dwindle, leading to a widening production gap. Therefore, accurately and quickly identifying sweet spots in these reservoirs is crucial for bridging this gap. Research indicates that previous methods and technologies for evaluating sweet spots in tight sandstone and conglomerate gas reservoirs are lacking. Existing methods for predicting sweet spots primarily rely on drawing circles centered on the wellbore and using well-controlled dynamic reserves as a basis for rough predictions based on distance. This results in low prediction accuracy and difficulties in efficient development, particularly for reservoirs with scattered residual gas distribution. Ultimately, this prevents precise delineation of sweet spots in tight sandstone and conglomerate gas reservoirs, hindering their sustainable and efficient development.

[0003] The CD gas field, a tight sandstone and conglomerate gas reservoir, was put into development in 2001. After 23 years of development, the well network density has continuously increased, and the well-controlled reserves have reached 4.9 billion cubic meters, accounting for 70% of the geological reserves. However, the remaining gas is gradually decreasing and scattered, and the distribution patterns of remaining gas and formation pressure are unclear. How to quickly and accurately identify remaining gas sweet spots, precisely guide well placement, further improve well control, and enhance the utilization of reserves in tight sandstone and conglomerate gas reservoirs has become an important issue. Therefore, it is urgent to establish a method for evaluating remaining gas sweet spots in tight sandstone and conglomerate gas reservoirs to guide precise well placement, which is of great significance for the continuous and efficient development of these reservoirs. Summary of the Invention

[0004] This invention proposes a method for evaluating the sweet spot of residual gas in tight sandstone and conglomerate gas reservoirs. This method addresses the problem that existing methods rely on drawing a circle centered on the wellbore and combining it with well-controlled dynamic reserves based on distance to make a rough prediction of the sweet spot, resulting in low prediction accuracy and the inability to verify the sweet spot.

[0005] According to one aspect of the present invention, a method for evaluating residual gas sweet spots in tight sandstone and conglomerate gas reservoirs is provided, comprising:

[0006] We obtained a detailed geological model and a numerical model of the original well network in the study area. Based on the detailed geological model of the original well network, we used structural models, sedimentary facies models, and reservoir models to select the static residual gas potential areas.

[0007] Based on the results of the static residual gas potential area selection, the current pressure distribution map and residual gas abundance distribution map of the original well network numerical model are used to determine the current residual gas potential area, and then the intersection with the static residual gas potential area is taken to select the current residual gas sweet spot area.

[0008] Based on the original well network numerical model, development scheme prediction is performed. Combining the cumulative production map, daily production map, pressure distribution map and remaining gas abundance distribution map, the optimal development scheme of the original well network numerical model is determined.

[0009] Based on the pressure distribution map and residual gas abundance distribution map predicted by the optimal development scheme of the original well network numerical model, the future residual gas potential area is determined, and then the intersection with the static residual gas potential area is taken to select the future residual gas sweet spot area.

[0010] The final remaining gas dessert area is determined by taking the intersection of the preferred current remaining gas dessert area and the preferred future remaining gas dessert area.

[0011] A virtual well is deployed in the final remaining gas sweet spot area to verify the final remaining gas sweet spot area.

[0012] Furthermore, before obtaining the detailed geological model of the original well network, a detailed geological model of the original well network is established. The method includes the following steps:

[0013] The original well network's detailed geological model includes: a structural model, a sedimentary facies model, and a reservoir model;

[0014] Obtain the structural model of the study area;

[0015] The construction model includes a plane model and a fault model;

[0016] Based on seismic interpretation horizon data, seismic velocity volume, and well point stratification data, the time-domain seismic interpretation horizon data is converted into depth-domain seismic interpretation horizon data through seismic velocity volume. The depth-domain seismic interpretation horizon data is then corrected using well point stratification data, and a horizon model is established by combining well and seismic data.

[0017] Based on seismic fault data, seismic velocity volume, and wellpoint fault data, the time-domain seismic fault data is converted into depth-domain seismic fault data through seismic velocity volume, and the depth-domain seismic fault data is corrected with wellpoint fault data. A fault model is established by combining well and seismic data.

[0018] Obtain sedimentary facies models for the study area;

[0019] A sedimentary facies model is established by using a region assignment method on the digitized sedimentary facies map;

[0020] Obtain reservoir models for the study area;

[0021] The reservoir model includes a favorable reservoir model and an effective reservoir model;

[0022] The favorable reservoir refers to a reservoir that includes a gas layer, a gas-diffusion layer, a gas-diffusion boundary layer, and a water layer.

[0023] The effective reservoir refers to a reservoir that includes a gas layer, a gas-differential layer, and a gas-differential boundary layer.

[0024] Well logging interpretation results include gas layers, differential gas layers, differential gas boundary layers, and water layers. Based on the well logging interpretation results, well points are mainly based on the interpretation results, and sedimentary facies control is used between wells. Combined with Gaussian stochastic simulation algorithm, interpolation is performed to establish favorable reservoir models. Then, the favorable reservoir models are used as synergistic conditions to establish effective reservoir models.

[0025] Furthermore, the method for selecting static residual gas potential zones using the aforementioned structural model, sedimentary facies model, and reservoir model includes the following steps:

[0026] The static residual gas potential zone was selected by taking the simultaneous fulfillment of three criteria: the structure is 60m above the gas-water interface, the sedimentary microfacies type is distributary channel facies, and the reservoir type is better than the gas boundary layer.

[0027] If the three conditions of the structure being 60m above the gas-water interface, the sedimentary microfacies being distributary channel facies, and the reservoir type being superior to the gas boundary layer cannot be met simultaneously, then the reservoir type being superior to the gas boundary layer is taken as the first factor, the sedimentary microfacies being distributary channel facies as the second factor, and the structure being 60m above the gas-water interface as the third factor, and the static residual gas potential zone is determined by prioritizing these factors in that order.

[0028] Furthermore, before obtaining the original well network numerical model, the original well network numerical model is built and historically fitted.

[0029] Furthermore, before building the original well network digital model, the original well network fine geological model is coarsened to obtain the original well network coarsened geological model. The method includes the following steps:

[0030] A fine geological model of the original well network in the study area is obtained. Based on the results of the fine geological model of the original well network, a coarse geological model of the original well network is generated by coarsening the grid.

[0031] Based on the three parameters of non-orthogonality of the grid, negative volume of the grid, and grid distortion, the quality control of the original well grid coarsening geological model is carried out to ensure that the original well grid coarsening geological model meets the requirements for establishing the original well grid numerical model.

[0032] Furthermore, the method for quality control of the coarsened geological model of the original well network includes the following steps:

[0033] The term "non-orthogonal grid" refers to the degree to which the grid angle deviates from 90°. When its absolute value is less than 30°, it indicates that the grid orthogonality is relatively good.

[0034] The negative volume of the grid indicates whether the grid volume is positive or negative. When the grid volume is greater than 0, it means that the grid has no negative volume.

[0035] The mesh distortion deformation represents the magnitude of the mesh distortion change. When the mesh distortion deformation is near 0, it indicates that there is no mesh distortion deformation.

[0036] Furthermore, after coarsening the original well network's fine geological model, dynamic data is loaded to obtain dynamic data for the target layer; the dynamic data includes: production data, measures data, and rock fluid parameters;

[0037] The splitting coefficient is determined based on the ratio of the formation coefficient of each gas-producing segment to the formation coefficient of the total gas-producing segments. Combined with the gas production at the wellhead, the production of the target segment at the wellhead is split to obtain the production data of the target segment; the formation coefficient = effective thickness × permeability.

[0038] The data on the measures include data on perforation, perforation repair, fracturing, and acidizing.

[0039] Based on the implementation records of measures at the target level, obtain the measure data for the target level;

[0040] The rock fluid parameters include PVT parameters, rock compressibility coefficient, and relative permeability curve parameter data;

[0041] Based on high-pressure physical property tests and relative permeability tests of the core samples from the target layer, PVT parameters, rock compressibility coefficient, and relative permeability curve parameters of the target layer were obtained.

[0042] Furthermore, the method for constructing the original well network numerical model includes the following steps:

[0043] Selection of original well network numerical model type;

[0044] The initialization conditions for the original well network numerical model have been determined;

[0045] Development strategy for the original well network digital model.

[0046] Furthermore, the selection of the original well network numerical model type is as follows:

[0047] Based on the fact that the study area is a tight sandstone-conglomerate gas reservoir, which is a normal pressure gas reservoir with stable internal components, the model type was selected, and the result was a black oil model with three-phase flow of oil, gas and water.

[0048] Furthermore, the initialization conditions of the original well network numerical model include the balanced method and the unbalanced enumeration method;

[0049] The equilibrium method refers to a situation where the air-water interface is composed of air above and water below, the initial state of the air and water is stable and does not flow, and the initial water saturation of the model is calculated according to the bound water saturation value of the relative permeability curve.

[0050] The non-equilibrium enumeration method refers to the initial unstable state of air and water, where flow occurs, and the initial water saturation of the model is calculated according to the given value, rather than according to the bound water saturation value of the relative permeability curve.

[0051] Based on the fact that gas reservoirs are initially stable and do not flow after formation, the equilibrium method is determined as the initialization condition.

[0052] Furthermore, the development strategy for the original well network numerical model includes constant pressure reduction and constant production reduction.

[0053] The so-called constant pressure reduction refers to reducing output to maintain constant pressure;

[0054] The term "fixed production pressure reduction" refers to maintaining a constant output by reducing pressure.

[0055] Based on the needs of gas reservoir development, formulate a development strategy of fixed production and pressure reduction.

[0056] Furthermore, the historical fitting of the original well network numerical model includes reserve fitting, production fitting, and pressure fitting.

[0057] Furthermore, the aforementioned reserve fitting refers to the fitting of the reserves in the original well network numerical model after coarsening with the reserves in the original well network fine geological model before coarsening.

[0058] Based on the fitting results, by adjusting the net-to-gross ratio parameter, the reserves of the original well network numerical model after coarsening are compared with the reserves of the original well network fine geological model before coarsening, so that the reserves of the original well network numerical model after coarsening meet the error requirements and the reserves are fitted.

[0059] The production fitting includes production fitting for the study area and production fitting for individual wells;

[0060] Based on the comparative analysis of the simulation curves of the original well network numerical model of cumulative production and daily production in the study area and the actual production data curves, the permeability of each zone is adjusted to make the simulation curves of the original well network numerical model of cumulative production and daily production in the study area fit the actual production data curves.

[0061] Based on the comparative analysis of the simulation curves of the original well network numerical model of cumulative production and daily production of single wells and the actual production data curves, the permeability and conductivity of single wells are adjusted to make the simulation curves of the original well network numerical model of cumulative production and daily production of single wells fit the actual production data curves.

[0062] The pressure fitting refers to the fitting of the simulated pressure of the original well network numerical model of a single well with the actual monitored pressure of the single well.

[0063] Based on the comparative analysis of the simulated pressure curves of the original well network digital model and the actual monitored pressure curves of the single well, the permeability and conductivity of the single well are adjusted to fit the simulated pressure curves of the original well network digital model with the actual monitored pressure curves of the single well.

[0064] Furthermore, based on the optimization results of the static remaining gas potential area, the current remaining gas potential area is determined, and then the intersection with the static remaining gas potential area is taken to optimize the current remaining gas sweet spot area. The specific method includes the following steps:

[0065] Based on the current pressure distribution map and remaining gas abundance distribution map of the original well network numerical model, the current remaining gas potential area is selected.

[0066] On the pressure distribution map of the original well network numerical model, find the region with the minimum pressure drop;

[0067] On the residual gas abundance distribution map of the original well network numerical model, find the region with the minimum decrease in residual gas abundance;

[0068] The intersection of the region with the minimum pressure drop and the region with the minimum decrease in remaining gas abundance is used to obtain the current remaining gas potential area.

[0069] By combining the current remaining gas potential area with the static remaining gas potential area, the current remaining gas sweet spot area is obtained.

[0070] Furthermore, based on the original well network numerical model, development scheme prediction is performed. Combining the cumulative production map, daily production map, pressure distribution map, and remaining gas abundance distribution map, the optimal development scheme of the original well network numerical model is determined. The method includes the following steps:

[0071] The original well network numerical model includes several development schemes;

[0072] For all the above-mentioned original well network numerical model development schemes, the development schemes for the future period are predicted. The cumulative production, daily production, pressure distribution map and remaining gas abundance map of the study area are compared. The development scheme with the highest cumulative production, the longest stable daily production time, and the largest decrease in pressure and remaining gas abundance in the study area is determined as the best development scheme of the original well network numerical model.

[0073] If the four parameters of maximum cumulative production, longest stable daily production, greatest pressure drop, and greatest decrease in remaining gas abundance cannot occur simultaneously, then the optimal development scheme of the original well network numerical model will be determined by prioritizing maximum cumulative production, the second factor, the third factor, the third factor, and the fourth factor, in that order.

[0074] Furthermore, based on the optimal development scheme of the determined original well network numerical model, the pressure distribution and residual gas abundance distribution over a future period are predicted, the future residual gas potential area is optimized, and then the intersection with the static residual gas potential area is taken to obtain the future residual gas sweet spot area. The method includes the following steps:

[0075] Based on the optimal development scheme of the original well network numerical model, predict the pressure distribution and remaining gas abundance distribution for a period of time in the future;

[0076] Based on the optimal development scheme of the original well network numerical model, the area with the minimum pressure drop is identified on the pressure distribution map for a predicted period of time in the future.

[0077] Based on the optimal development scheme of the original well network numerical model, the region with the smallest decrease in remaining gas abundance is found on the distribution map of remaining gas abundance in the future period.

[0078] The intersection of the region with the minimum pressure drop and the region with the minimum decrease in remaining gas abundance is used to obtain the potential area of ​​future remaining gas.

[0079] By combining the future remaining gas potential area with the static remaining gas potential area, the future remaining gas sweet spot area is obtained.

[0080] Furthermore, a virtual well is deployed in the obtained final remaining gas sweet spot area to verify the final remaining gas sweet spot area. The method includes the following steps:

[0081] Obtain virtual well parameter settings;

[0082] The virtual well parameter settings include: perforation interval, fracturing interval, single well permeability, conductivity coefficient, and reasonable production data;

[0083] After deploying virtual wells, a new well network digital model is formed. Through an effective reservoir model, reference wells for the virtual wells are found, and data on the perforation interval, fracturing interval, single-well permeability, conductivity coefficient, and reasonable production rate of the virtual wells are determined.

[0084] Based on the effective reservoir model profiles of the virtual well and the reference well, we find the effective reservoir development intervals, perform perforation and fracturing, and set reasonable production data for the virtual well.

[0085] Based on the reference well, set the single-well permeability and conductivity coefficient of the virtual well;

[0086] After the virtual well parameters are set, the optimal development scheme for the new well network digital model is selected on the new well network digital model to obtain the best development scheme for the new well network digital model.

[0087] Based on the optimal development scheme of the new well network numerical model, the pressure distribution map and the remaining gas abundance distribution map for a predicted future period are obtained; if the pressure and remaining gas abundance of the new well network numerical model at the virtual well are significantly lower than those of the original well network numerical model, the final remaining gas sweet spot is verified.

[0088] Furthermore, based on the new well network numerical model, the optimal development scheme for the new well network numerical model is selected to obtain the best development scheme for the new well network numerical model. The method includes the following steps:

[0089] The new well network digital model includes several development schemes;

[0090] For all the above-mentioned new well network numerical model development schemes, the development scheme prediction for a future period of time is carried out. The cumulative production, daily production, pressure distribution map and remaining gas abundance map of the study area are compared and the development scheme with the highest cumulative production, the longest stable daily production time, and the largest pressure drop and remaining gas abundance drop in the study area is determined as the best development scheme for the new well network numerical model.

[0091] If the four parameters of maximum cumulative production, longest stable daily production, greatest pressure drop, and greatest decrease in remaining gas abundance cannot occur simultaneously, then the optimal development scheme for the new well network numerical model will be determined by prioritizing maximum cumulative production as the first factor, greatest decrease in remaining gas abundance as the second factor, longest stable daily production as the third factor, and greatest pressure drop as the fourth factor.

[0092] Furthermore, the "future period" refers to 20 years from now.

[0093] According to one aspect of the present invention, an apparatus for evaluating the residual gas sweet spot region of a tight sandstone-conglomerate gas reservoir is provided, comprising:

[0094] The first optimization unit is used to obtain the original well network fine geological model and the original well network numerical model of the study area. Based on the original well network fine geological model, the static residual gas potential area is optimized by using the structural model, sedimentary facies model and reservoir model.

[0095] The second optimization unit is used to determine the current remaining gas potential area based on the optimization results of the static remaining gas potential area, using the current pressure distribution map and remaining gas abundance distribution map of the original well network numerical model, and then take the intersection with the static remaining gas potential area to optimize the current remaining gas sweet spot area.

[0096] The first determining unit is used to predict the development scheme based on the original well network numerical model, and to determine the optimal development scheme of the original well network numerical model by combining the cumulative production map, daily production map, pressure distribution map and remaining gas abundance distribution map.

[0097] The third optimization unit is used to determine the future residual gas potential area on the pressure distribution map and residual gas abundance distribution map predicted by the optimal development scheme of the original well network numerical model, and then take the intersection with the static residual gas potential area to optimize the future residual gas sweet spot area.

[0098] The second determining unit is used to take the intersection of the preferred current remaining gas dessert area and the preferred future remaining gas dessert area to determine the final remaining gas dessert area.

[0099] A verification unit is used to deploy a virtual well in the final remaining gas sweet spot area to verify the final remaining gas sweet spot area.

[0100] The present invention has at least the following beneficial effects:

[0101] This invention proposes a method and apparatus for evaluating the sweet spot region of residual gas in tight sandstone and conglomerate gas reservoirs.

[0102] Based on the refined geological model of the original well network, static residual gas potential zones are selected using structural, sedimentary facies, and reservoir models. Based on the static residual gas potential zone selection results, the current residual gas potential zones are determined using the current pressure and residual gas abundance distribution maps of the original well network numerical model. The intersection of these maps with the static residual gas potential zones is then used to select the current residual gas sweet spot zones. Based on the original well network numerical model, development scheme predictions are performed. Combining cumulative production, daily production, pressure, and residual gas abundance distribution maps, the optimal development scheme of the original well network numerical model is determined. On the pressure and residual gas abundance distribution maps predicted by the optimal development scheme of the original well network numerical model, future residual gas potential zones are determined. The intersection of these maps with the static residual gas potential zones is then used to select the future residual gas sweet spot zones. The intersection of the current and future residual gas sweet spot zones is used to determine the final residual gas sweet spot zone. Virtual wells are deployed in the final residual gas sweet spot zone to verify its validity. This invention clarifies the current remaining gas sweet spot, the future remaining gas sweet spot, and determines the final remaining gas sweet spot, realizing a systematic, accurate, and rapid selection of remaining gas sweet spot in tight sandstone and conglomerate gas reservoirs, and precisely guiding well location deployment.

[0103] The method for evaluating the remaining sweet spot of tight sandstone and conglomerate gas reservoirs in this invention can be used for well site deployment, reducing well placement risks, improving drilling success rates, and realizing the efficient development of gas wells.

[0104] Using the method of this invention, three final remaining gas sweet spots were found in the CD gas field, a tight sandstone and conglomerate gas reservoir. Well P6 was deployed in sweet spot 1, well 6-5 in sweet spot 2, and well P5 in sweet spot 3. All three wells have been completed and have yielded industrial gas flow, achieving a 100% success rate in encountering industrial gas flow wells. The well-controlled reserves of these three wells are 1.5 billion cubic meters, bringing the total well-controlled reserves of the CD gas field to 6.4 billion cubic meters, representing 91% of the geological reserves. This demonstrates the high-efficiency and sustainable development of the tight sandstone and conglomerate gas reservoir. Attached Figure Description

[0105] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present invention and, together with the specification, serve to explain the technical solutions of the present invention.

[0106] Figure 1 A flowchart illustrating a method for evaluating residual gas sweet spots in tight sandstone and conglomerate gas reservoirs according to an embodiment of the present invention;

[0107] Figure 2 A preferred diagram of the static residual gas potential zone according to an embodiment of the present invention is shown, wherein a is a structural model and a static potential zone; b is a sedimentary facies model and a static potential zone; and c is an effective reservoir model and a static potential zone. Figure 3 A preferred diagram of the current remaining gas potential region according to an embodiment of the present invention is shown, wherein a is a pressure distribution diagram of the current potential region; b is a remaining gas abundance distribution diagram of the current potential region of layer 1; and c is a remaining gas abundance distribution diagram of the current potential region of layer 2.

[0108] Figure 4 A preferred diagram of the current remaining gas sweet spot region according to an embodiment of the present invention is shown, wherein a is a pressure distribution diagram of the current sweet spot region; b is a remaining gas abundance distribution diagram of the current sweet spot region in layer 1; and c is a remaining gas abundance distribution diagram of the current sweet spot region in layer 2.

[0109] Figure 5 The diagram illustrates the optimal development scheme of the original well network numerical model according to an embodiment of the present invention, where a is the cumulative gas production curve of the original well network numerical model in the study area; b is the daily gas production curve of the original well network numerical model in the study area; and c is the formation pressure drop curve of the original well network numerical model in the study area.

[0110] Figure 6 A preferred diagram of the future remaining gas potential area according to an embodiment of the present invention is shown, wherein a is a pressure distribution diagram of the future potential area; b is a remaining gas abundance distribution diagram of the future potential area of ​​layer 1; and c is a remaining gas abundance distribution diagram of the future potential area of ​​layer 2.

[0111] Figure 7A preferred diagram of the future residual gas sweet spot region according to an embodiment of the present invention is shown, wherein a is a pressure distribution diagram of the future sweet spot region; b is a residual gas abundance distribution diagram of the future sweet spot region in layer 1; and c is a residual gas abundance distribution diagram of the future sweet spot region in layer 2.

[0112] Figure 8 A preferred diagram of the final residual gas sweet spot region according to an embodiment of the present invention is shown, wherein a is a pressure distribution diagram of the final sweet spot region; b is a residual gas abundance distribution diagram of the final sweet spot region of layer 1; and c is a residual gas abundance distribution diagram of the final sweet spot region of layer 2.

[0113] Figure 9 The diagram illustrates the optimal development scheme of the new well network numerical model according to an embodiment of the present invention, where a is the cumulative gas production curve of the new well network numerical model in the study area; b is the daily gas production curve of the new well network numerical model in the study area; and c is the formation pressure drop curve of the new well network numerical model in the study area.

[0114] Figure 10 The diagram illustrates the final residual gas sweet spot map for verifying the new well pattern based on pressure and residual gas abundance changes according to an embodiment of the present invention, where a is the average pressure decrease map of the study area and well points; b is the average residual gas abundance decrease map of the study area and well points in layer 1; and c is the average residual gas abundance decrease map of the study area and well points in layer 2. Detailed Implementation

[0115] Various exemplary embodiments, features, and aspects of the present invention will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0116] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0117] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0118] Furthermore, to better illustrate the present invention, numerous specific details are set forth in the following detailed embodiments. Those skilled in the art will understand that the present invention can be practiced without certain specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order to highlight the spirit of the invention.

[0119] Figure 1 A flowchart illustrating a method for evaluating residual gas sweet spots in tight sandstone and conglomerate gas reservoirs according to an embodiment of the present invention; Figure 2 A preferred diagram of the static residual gas potential zone according to an embodiment of the present invention is shown, wherein a is a structural model and a static potential zone; b is a sedimentary facies model and a static potential zone; and c is an effective reservoir model and a static potential zone. Figure 3 A preferred diagram of the current remaining gas potential region according to an embodiment of the present invention is shown, wherein a is a pressure distribution diagram of the current potential region; b is a remaining gas abundance distribution diagram of the current potential region of layer 1; and c is a remaining gas abundance distribution diagram of the current potential region of layer 2. Figure 4 A preferred diagram of the current remaining gas sweet spot region according to an embodiment of the present invention is shown, wherein a is a pressure distribution diagram of the current sweet spot region; b is a remaining gas abundance distribution diagram of the current sweet spot region in layer 1; and c is a remaining gas abundance distribution diagram of the current sweet spot region in layer 2. Figure 5 The diagram illustrates the optimal development scheme of the original well network numerical model according to an embodiment of the present invention, where a is the cumulative gas production curve of the original well network numerical model in the study area; b is the daily gas production curve of the original well network numerical model in the study area; and c is the formation pressure drop curve of the original well network numerical model in the study area. Figure 6 A preferred diagram of the future remaining gas potential area according to an embodiment of the present invention is shown, wherein a is a pressure distribution diagram of the future potential area; b is a remaining gas abundance distribution diagram of the future potential area of ​​layer 1; and c is a remaining gas abundance distribution diagram of the future potential area of ​​layer 2. Figure 7 A preferred diagram of the future residual gas sweet spot region according to an embodiment of the present invention is shown, wherein a is a pressure distribution diagram of the future sweet spot region; b is a residual gas abundance distribution diagram of the future sweet spot region in layer 1; and c is a residual gas abundance distribution diagram of the future sweet spot region in layer 2. Figure 8 A preferred diagram of the final residual gas sweet spot region according to an embodiment of the present invention is shown, wherein a is a pressure distribution diagram of the final sweet spot region; b is a residual gas abundance distribution diagram of the final sweet spot region of layer 1; and c is a residual gas abundance distribution diagram of the final sweet spot region of layer 2. Figure 9 The diagram illustrates the optimal development scheme of the new well network numerical model according to an embodiment of the present invention, where a is the cumulative gas production curve of the new well network numerical model in the study area; b is the daily gas production curve of the new well network numerical model in the study area; and c is the formation pressure drop curve of the new well network numerical model in the study area. Figure 10The diagram illustrates the final residual gas sweet spot map for verifying the new well pattern based on pressure and residual gas abundance changes according to an embodiment of the present invention, where a is the average pressure decrease map of the study area and well points; b is the average residual gas abundance decrease map of the study area and well points in layer 1; and c is the average residual gas abundance decrease map of the study area and well points in layer 2.

[0120] like Figure 1-10 As shown, a method for evaluating the sweet spot region of residual gas in tight sandstone and conglomerate gas reservoirs includes the following steps:

[0121] Step S01: Obtain the detailed geological model and numerical model of the original well network in the study area. Based on the detailed geological model, use the structural model, sedimentary facies model, and reservoir model to select the static remaining gas potential area. Step S02: Based on the selected static remaining gas potential area, use the current pressure distribution map and remaining gas abundance distribution map of the original well network numerical model to determine the current remaining gas potential area. Then, take the intersection with the static remaining gas potential area to select the current remaining gas sweet spot area. Step S03: Based on the original well network numerical model, predict the development plan and combine it with the cumulative production. Based on the graphs, daily production graphs, pressure distribution graphs, and remaining gas abundance distribution graphs, determine the optimal development scheme for the original well network numerical model; Step S04: On the pressure distribution graphs and remaining gas abundance distribution graphs predicted by the optimal development scheme of the original well network numerical model, determine the future remaining gas potential area, and then take the intersection with the static remaining gas potential area to select the future remaining gas sweet spot area; Step S05: Take the intersection of the selected current remaining gas sweet spot area and the selected future remaining gas sweet spot area to determine the final remaining gas sweet spot area; Step S06: Deploy virtual wells in the final remaining gas sweet spot area to verify the final remaining gas sweet spot area.

[0122] The method for evaluating residual gas sweet spots in tight sandstone and conglomerate gas reservoirs provided in this embodiment of the invention specifically includes the following steps:

[0123] Step S01: Obtain the fine geological model and numerical model of the original well network in the study area. Based on the fine geological model of the original well network, use the structural model, sedimentary facies model and reservoir model to select the static residual gas potential area.

[0124] In this invention, before obtaining the fine geological model of the original well network, a fine geological model of the original well network is established to obtain the fine geological model of the original well network. The method includes the following steps:

[0125] The original well network's detailed geological model includes: a structural model, a sedimentary facies model, and a reservoir model;

[0126] Obtain the construction model of the study area:

[0127] The structural model includes a bedding plane model and a fault model. Based on seismic interpretation horizon data, seismic velocity volumes, and well point stratification data, the time-domain seismic interpretation horizon data is converted into depth-domain seismic interpretation horizon data through seismic velocity volumes. The depth-domain seismic interpretation horizon data is then corrected using well point stratification data, and a bedding plane model is established by combining well and seismic data. Based on seismic fault data, seismic velocity volumes, and well point fault data, the time-domain seismic fault data is converted into depth-domain seismic fault data through seismic velocity volumes. The depth-domain seismic fault data is then corrected using well point fault data, and a fault model is established by combining well and seismic data.

[0128] Obtain the sedimentary facies model of the study area:

[0129] A sedimentary facies model is established by using a region assignment method on the digitized sedimentary facies map;

[0130] Obtain the reservoir model for the study area:

[0131] The reservoir model includes a favorable reservoir model and an effective reservoir model. A favorable reservoir refers to a reservoir containing a gas layer, a gas-diffusion layer, a gas-diffusion boundary layer, and a water layer. An effective reservoir refers to a reservoir containing a gas layer, a gas-diffusion layer, and a gas-diffusion boundary layer. Well logging interpretation conclusions include gas layers, gas-diffusion layers, gas-diffusion boundary layers, and water layers. Based on the well logging interpretation conclusions, well points are mainly based on the interpretation conclusions, and sedimentary facies control is used between wells. Combined with Gaussian stochastic simulation algorithm, interpolation is performed to establish a favorable reservoir model. Then, the favorable reservoir model is used as a co-condition to establish an effective reservoir model.

[0132] In this embodiment of the invention, taking the CD gas field, a tight sandstone and conglomerate gas reservoir, as an example, the target interval includes Layer 1 and Layer 2. The actual number of wells completed in the original well network of the study area is 11. A detailed geological model of the original well network is obtained by performing a detailed geological model of the original well network in the study area. The specific process includes:

[0133] 1. Constructing the model:

[0134] The construction models include: the plane model and the fault model.

[0135] (1) Bedding Model: Based on the seismic interpretation data of layers 1 and 2 in the study area, and the seismic velocity volume, the time-domain seismic interpretation data of layers 1 and 2 were converted into depth-domain seismic interpretation data of layers 1 and 2. At well points, based on the well point stratification data of layers 1 and 2 from the 11 completed wells in the study area, the depth-domain seismic interpretation data of layers 1 and 2 were corrected using the stratification data of layers 1 and 2. Between wells, using the depth-domain seismic interpretation data of layers 1 and 2 as the standard, a bedding model was established using a well-seismic combination method. The overall structural morphology of the study area is characterized by a west-high and east-low structure, with the highest and lowest points between -2900m and -3600m (e.g., ...). Figure 2 (As shown in a). Eleven actual drilling wells confirmed that there were no water layers in the entire area.

[0136] (2) Fault Model: Based on the seismic fault data and seismic velocity volumes of the study area, the time-domain seismic fault data was converted into depth-domain seismic fault data using seismic velocity volumes. At well points, the depth-domain seismic fault data was corrected using well-point fault data from 11 completed wells in the study area. Between wells, a fault model was established using the depth-domain converted seismic fault data as the standard, employing a well-seismic combination method. Faults are developed throughout the study area, with higher structural areas and lower structural areas, and the fault strike is predominantly near north-south (e.g., ...). Figure 2 (as shown in a).

[0137] 2. Sedimentary facies model:

[0138] Digitized sedimentary facies maps of the study area were used to establish sedimentary facies models using a regional assignment method. Three types of sedimentary microfacies were identified in the study area: distributary channel microfacies, interchannel microfacies, and floodplain microfacies. The most favorable microfacies in the study area were distributary channel microfacies, followed by interchannel microfacies, and lastly floodplain microfacies (e.g., distributary channel microfacies, interchannel microfacies, and floodplain microfacies). Figure 2 (as shown in b).

[0139] 3. Reservoir Model:

[0140] The study area's reservoir models include favorable reservoir models and effective reservoir models. Favorable reservoirs refer to reservoirs containing gas layers, differential gas layers, differential gas boundary layers, and water layers; effective reservoirs refer to reservoirs containing gas layers, differential gas layers, and differential gas boundary layers. Favorable reservoir models model and display the favorable reservoir conclusions from well logging interpretations; effective reservoir models model and display the effective reservoir conclusions from well logging interpretations. Specifically, based on well logging interpretation conclusions (including gas layers, differential gas layers, differential gas boundary layers, and water layers), well-point interpretations are prioritized, and sedimentary facies control is used between wells. Combined with Gaussian stochastic simulation algorithms, interpolation is performed to establish favorable reservoir models. Then, using the favorable reservoir models as co-conditions, effective reservoir models are established. Based on the effective reservoir classification of the study area (as shown in Table 1), and using the effective reservoir models as constraints, effective reservoir classification is performed at well-point locations using well logging curves, and interpolation is performed between wells using Gaussian stochastic simulation algorithms to obtain classified effective reservoir models (e.g., ...). Figure 2 (as shown in c).

[0141] Table 1

[0142]

[0143] In this invention, the method for selecting static residual gas potential zones using the aforementioned structural model, sedimentary facies model, and reservoir model includes the following steps:

[0144] Using the simultaneous fulfillment of three criteria—structure above 60m above the gas-water interface, sedimentary microfacies type as distributary channel facies, and reservoir type superior to differential gas boundary layer—as the standard, static residual gas potential zones are selected to obtain static residual gas potential zones.

[0145] If the three conditions of the structure being 60m above the gas-water interface, the sedimentary microfacies being distributary channel facies, and the reservoir type being superior to the gas boundary layer cannot be met simultaneously, then the reservoir type being superior to the gas boundary layer is taken as the first factor, the sedimentary microfacies being distributary channel facies as the second factor, and the structure being 60m above the gas-water interface as the third factor, and the static residual gas potential zone is determined by prioritizing these factors in that order.

[0146] In this embodiment of the invention, the static residual gas potential zone is determined by considering the structure, sedimentary microfacies type, and reservoir type that influence the selection of the static residual gas potential zone.

[0147] Based on the analysis of data from 11 drilled wells in the study area, and using criteria such as structural height > -3200m, sedimentary microfacies type as distributary channel microfacies, and reservoir type superior to Class II reservoirs, three static residual gas potential zones were identified (e.g., Figure 2 (As shown in a to c). If the structural height is > -3200m, the sedimentary microfacies type is distributary channel microfacies, and the reservoir type is superior to Class II reservoir, these three conditions cannot be met simultaneously. Therefore, the reservoir type being superior to Class II reservoir is taken as the first factor, the sedimentary microfacies type being distributary channel microfacies is taken as the second factor, and the structural height being > -3200m is taken as the third factor. The selection is carried out in this order to determine the static residual gas potential zone.

[0148] Step S02: Based on the results of the static residual gas potential area selection, the current residual gas potential area is determined by using the current pressure distribution map and residual gas abundance distribution map of the original well network numerical model. Then, the intersection of the residual gas potential area and the static residual gas potential area is taken to select the current residual gas sweet spot area.

[0149] In this invention, before obtaining the original well network numerical model, the original well network numerical model is built and the original well network numerical model is historically fitted.

[0150] In this invention, before building the original well network digital model, a coarsened geological model of the original well network is performed to obtain a coarsened geological model of the original well network. The method includes the following steps:

[0151] A fine geological model of the original well network in the study area is obtained. Based on the results of the fine geological model of the original well network, a coarse geological model of the original well network is generated by coarsening the grid.

[0152] Based on the three parameters of non-orthogonality of the grid, negative volume of the grid, and grid distortion, the quality control of the original well grid coarsening geological model is carried out to ensure that the original well grid coarsening geological model meets the requirements for establishing the original well grid numerical model.

[0153] In this invention, the method for quality control of the coarsened geological model of the original well network includes the following steps:

[0154] The term "non-orthogonal grid" refers to the degree to which the grid angle deviates from 90°. When its absolute value is less than 30°, it indicates that the grid orthogonality is relatively good.

[0155] The negative volume of the grid indicates whether the grid volume is positive or negative. When the grid volume is greater than 0, it means that the grid has no negative volume.

[0156] The mesh distortion deformation represents the magnitude of the mesh distortion change. When the mesh distortion deformation is near 0, it indicates that there is no mesh distortion deformation.

[0157] In this embodiment of the invention, the number of grids in the fine geological model of the original well network in the study area is 11.79 million, and the number of grids in the coarsened geological model of the original well network is 1.17 million, which reduces the total number of grids by about 90%.

[0158] In this embodiment of the invention, the grid angle in the study area is between 0.04° and 30°, and its absolute value is <30°, indicating good grid orthogonality; the grid volume is all >0, indicating that the grid has no negative volume; the grid distortion change is between 0 and 0.05, and near 0, indicating no grid distortion deformation.

[0159] In this invention, after coarsening the original well network fine geological model, dynamic data loading is performed to obtain dynamic data of the target layer.

[0160] The dynamic data includes: production data, measures data, and rock fluid parameters;

[0161] The splitting coefficient is determined based on the ratio of the formation coefficient of each gas-producing segment to the formation coefficient of the total gas-producing segments. Combined with the gas production at the wellhead, the production of the target segment at the wellhead is split to obtain the production data of the target segment; the formation coefficient = effective thickness × permeability.

[0162] The data on the measures include data on perforation, perforation repair, fracturing, and acidizing.

[0163] Based on the implementation records of measures in the target layer, obtain the measures data for the target layer; the rock fluid parameters include PVT parameters, rock compressibility coefficient and relative permeability curve parameters; based on the high-pressure physical property experiments and relative permeability experiments of the core of the target layer, obtain the PVT parameters, rock compressibility coefficient and relative permeability curve parameters for the target layer.

[0164] In this embodiment of the invention, part of the gas production of 5 wells in the study area comes from the upper layer, and it is necessary to calculate the production splitting coefficient of the target layer of these 5 wells.

[0165] Total stratigraphic coefficient = stratigraphic coefficient of upper section + stratigraphic coefficient of target section;

[0166] The yield splitting factor of the target section = the stratigraphic factor of the target section / the total stratigraphic factor.

[0167] The gas production of the target formation in the study area of ​​each well is obtained by multiplying the production splitting coefficient of the target formation of each well by the daily gas production of that well.

[0168] In this embodiment of the invention, the measures implementation record for the target layer is used to obtain the measures data for the target layer. Based on the high-pressure physical property test and relative permeability test of the core sample of the target layer, the PVT parameters, rock compressibility coefficient, and relative permeability curve parameters of the target layer are obtained.

[0169] In this invention, the method for constructing the original well network digital model includes the following steps:

[0170] Selection of original well network numerical model type;

[0171] The initialization conditions for the original well network numerical model have been determined;

[0172] Development strategy for original well network numerical model;

[0173] The type of original well network numerical model is selected as follows:

[0174] Based on the fact that the study area is a tight sandstone and conglomerate gas reservoir, which is a normal pressure gas reservoir with stable internal components, the model type was selected, and the result was a black oil model with three-phase flow of oil, gas and water.

[0175] The initialization conditions of the original well network numerical model include the balanced method and the unbalanced enumeration method;

[0176] The equilibrium method refers to a situation where the air-water interface is composed of air above and water below, the initial state of the air and water is stable and does not flow, and the initial water saturation of the model is calculated according to the bound water saturation value of the relative permeability curve.

[0177] The non-equilibrium enumeration method refers to the initial state of gas and water being unstable and undergoing flow. The initial water saturation of the model is calculated according to the given value, rather than according to the bound water saturation value of the relative permeability curve. Based on the fact that the initial state of a gas reservoir is stable after its formation and does not undergo flow, the equilibrium method is determined as the initialization condition.

[0178] The original well network digital model development strategy includes pressure-controlled production reduction and production-controlled pressure reduction;

[0179] The term "pressure-controlled production reduction" refers to reducing production while maintaining constant pressure; "production-controlled pressure reduction" refers to reducing pressure while maintaining constant production; a production-controlled pressure reduction development strategy is formulated based on the gas reservoir development needs; and the original well network digital model is completed.

[0180] In this embodiment of the invention, the study area is a tight sandstone-conglomerate gas reservoir, belonging to an atmospheric pressure gas reservoir with stable internal components. A model type was selected, and the result was a black oil model with three-phase flow of oil, gas, and water. Given that the initial state of the gas reservoir in the study area is stable and non-flowing, the equilibrium method was determined as the initialization condition. Based on the gas reservoir development requirements, a development strategy of fixed production and pressure reduction was formulated.

[0181] In this invention, the historical fitting of the original well network numerical model includes reserve fitting, production fitting and pressure fitting.

[0182] The aforementioned reserve fitting refers to the fitting of the reserves in the original well network numerical model after coarsening with the reserves in the original well network fine geological model before coarsening. Based on the fitting results, the net-to-gross ratio parameter is adjusted to compare the reserves in the original well network numerical model after coarsening with the reserves in the original well network fine geological model before coarsening, so that the reserves in the original well network numerical model after coarsening meet the error requirements, thereby achieving reserve fitting.

[0183] The production fitting includes production fitting for the study area and production fitting for individual wells. Based on the comparative analysis of the simulation curves of the original well network numerical model of cumulative production and daily production in the study area with the actual production data curves, the permeability of each zone is adjusted to fit the simulation curves of the original well network numerical model of cumulative production and daily production in the study area with the actual production data curves. Based on the comparative analysis of the simulation curves of the original well network numerical model of cumulative production and daily production for individual wells with the actual production data curves, the permeability and conductivity of individual wells are adjusted to fit the simulation curves of the original well network numerical model of cumulative production and daily production for individual wells with the actual production data curves.

[0184] The pressure fitting refers to the fitting of the simulated pressure of the original well network digital model with the actual monitored pressure of the single well. Based on the comparative analysis results of the simulated pressure curve of the original well network digital model and the actual monitored pressure curve of the single well, the permeability and conductivity of the single well are adjusted to fit the simulated pressure curve of the original well network digital model with the actual monitored pressure curve of the single well.

[0185] In this embodiment of the invention, the reserves of the original well network numerical model after coarsening in the study area are 92.0 × 10⁻⁶. 8 m 3 The original well network's fine geological model, before coarsening, showed reserves of 89.7 × 10⁻⁶. 8 m 3 The error between the two is 2.5%, and the reserve fitting of the study area is within the error range, meeting the accuracy requirements.

[0186] Based on the reserve fitting of the study area, the cumulative production and daily production of the study area were fitted. The cumulative production and daily production simulated by the original well network numerical model were both lower than the actual production data. Analysis suggests that the low production simulated by the original well network numerical model, under the condition of reserve fitting, may be due to insufficient seepage conditions. Considering the development of faults and the segmentation of the reservoir, a fault zoning setting was adopted. The permeability and fault conductivity of different fault zoning zones were adjusted to gradually approximate the actual production data, ultimately achieving a close fit between the cumulative production and daily production simulated by the original well network numerical model and the actual production data.

[0187] Based on the fitting of cumulative and daily production data in the study area, the permeability and conductivity of individual wells were adjusted to ensure that the cumulative and daily production data simulated by the original well network numerical model in the study area were consistent with the actual production data. Simultaneously, the simulated pressure of the original well network numerical model in the study area was fitted with the actual monitored pressure of individual wells, ultimately completing the historical fitting of the original well network numerical model.

[0188] In this invention, based on the optimized static remaining gas potential area, the current remaining gas potential area is determined, and then the intersection of this area and the static remaining gas potential area is used to optimize the current remaining gas sweet spot area. The specific method includes the following steps:

[0189] Based on the current pressure distribution map and remaining gas abundance distribution map of the original well network numerical model, the current remaining gas potential area is selected.

[0190] On the pressure distribution map of the original well network numerical model, find the region with the minimum pressure drop;

[0191] On the residual gas abundance distribution map of the original well network numerical model, find the region with the minimum decrease in residual gas abundance;

[0192] The intersection of the region with the minimum pressure drop and the region with the minimum decrease in remaining gas abundance is used to obtain the current remaining gas potential area.

[0193] By combining the current remaining gas potential area with the static remaining gas potential area, the current remaining gas sweet spot area is obtained.

[0194] In this embodiment of the invention, the original well network numerical model of the study area shows that the average pressure in the study area in 2004 was 36.99 MPa, and the average pressure in the study area in 2024 was 35.04 MPa, a decrease of 5.3% compared to 2004. The area with the smallest pressure decrease is mainly concentrated in the northern part of the study area between CXP3~6-1~P2 (e.g., Figure 3 (As shown in a). The average residual gas abundance in Layer 1 of the study area was 1.2349 in 2004 and 1.1544 in 2024, a decrease of 6.5% compared to 2004. The area with the smallest decrease in residual gas abundance was mainly concentrated in the northern part of the study area between CXP3~6-1~P2 (e.g., Figure 3 (As shown in b). The average residual gas abundance in Layer 2 of the study area was 0.3698 in 2004 and 0.3604 in 2024, a decrease of 2.5% compared to 2004. The areas with the smallest decrease in residual gas abundance were mainly concentrated around 6-1 in the northern part of the study area (e.g., Figure 3 (as shown in c).

[0195] By intersecting the region with the smallest pressure drop with the region with the smallest decrease in residual gas abundance in layer 1, three residual gas potential regions are obtained for layer 1. The residual gas abundance of residual gas potential region 1 is currently 3.74, that of residual gas potential region 2 is 3.13, and that of residual gas potential region 3 is 4.38 (e.g., Figure 3 (As shown in b). The intersection of the region with the smallest pressure drop and the region with the smallest decrease in remaining gas abundance in layer 2 yields one current remaining gas potential region in layer 2. To correspond with layer 1, this current remaining gas potential region in layer 2 is named Current Remaining Gas Potential Region 2, and its remaining gas abundance is 3.66 (e.g., ...). Figure 3 (as shown in c).

[0196] Take the intersection of the current remaining gas potential area of ​​layer 1 and the current remaining gas potential area of ​​layer 2 with the static remaining gas potential area to obtain the current remaining gas sweet spot area (e.g. Figure 4 (as shown in a to c).

[0197] Step S03: Based on the original well network numerical model, predict the development scheme, and determine the optimal development scheme of the original well network numerical model by combining the cumulative production map, daily production map, pressure distribution map, and remaining gas abundance distribution map; the method includes the following steps:

[0198] The original well network numerical model includes several development schemes;

[0199] For all the above-mentioned original well network numerical model development schemes, the development schemes for the future period are predicted. The cumulative production, daily production, pressure distribution map and remaining gas abundance map of the study area are compared. The development scheme with the highest cumulative production, the longest stable daily production time, and the largest decrease in pressure and remaining gas abundance in the study area is determined as the best development scheme of the original well network numerical model.

[0200] If the four parameters of maximum cumulative production, longest stable daily production, greatest pressure drop, and greatest decrease in remaining gas abundance cannot occur simultaneously, then the optimal development scheme of the original well network numerical model will be determined by prioritizing maximum cumulative production, the second factor, the third factor, the third factor, and the fourth factor, in that order.

[0201] In this embodiment of the invention, the daily production of 11 wells in the study area was set, divided into three schemes: Scheme 1, Scheme 2, and Scheme 3. Based on the original well network numerical model, the scheme prediction was carried out, with the prediction period from June 2024 to June 2044, and the reporting frequency was 1 year, for a total of 20 years of scheme prediction (the scheme prediction of the original well network numerical model is shown in Table 2).

[0202] Table 2:

[0203]

[0204] Based on single-well predictions, five wells in the study area will have higher cumulative production under Scheme 1, while six wells in the study area will have higher cumulative production under Scheme 3.

[0205] Based on the cumulative production forecast for the study area, the cumulative production of Scheme 1 is 2.34 billion cubic meters, Scheme 2 is 2.33 billion cubic meters, and Scheme 3 is 2.41 billion cubic meters. Scheme 3 has the highest cumulative production, exceeding Scheme 1 by 0.7 billion cubic meters (e.g., ...). Figure 5 (as shown in a).

[0206] Based on the predicted daily output stabilization period for the study area, Scheme 1 has no daily output stabilization period, Scheme 2 has a daily output stabilization period of 4 years, and Scheme 3 has a daily output stabilization period of 12 years. Scheme 3 has the longest daily output stabilization period (e.g., ...). Figure 5 (as shown in b).

[0207] Based on the predicted pressure reduction in the study area, the pressure under Scenario 1 will decrease from 35.04 MPa in 2024 to 27.88 MPa in 2044, an average decrease of 17.6%; under Scenario 2, the pressure will decrease from 35.04 MPa in 2024 to 27.99 MPa in 2044, an average decrease of 17.3%; and under Scenario 3, the pressure will decrease from 35.04 MPa in 2024 to 27.79 MPa in 2044, an average decrease of 17.8%. Scenario 3 represents the largest pressure reduction (e.g., ...). Figure 5 (as shown in c).

[0208] Based on the predicted residual gas abundance in the study area, the residual gas abundance in Layer 1 under Scenario 1 is projected to decrease from 1.1544 in 2024 to 0.9040 in 2044, an average decrease of 21.7%; under Scenario 2, the residual gas abundance in Layer 1 is projected to decrease from 1.1544 in 2024 to 0.9067 in 2044, an average decrease of 21.5%; and under Scenario 3, the residual gas abundance in Layer 1 is projected to decrease from 1.1544 in 2024 to 0.8986 in 2044, an average decrease of 22.2%. In Scheme 1, the residual gas abundance in layer 2 decreased from 0.3604 in 2024 to 0.3246 in 2044, an average decrease of 9.9%. In Scheme 2, the residual gas abundance in layer 2 decreased from 0.3604 in 2024 to 0.3229 in 2044, an average decrease of 10.4%. In Scheme 3, the residual gas abundance in layer 2 decreased from 0.3604 in 2024 to 0.3185 in 2044, an average decrease of 11.6%. The residual gas abundance in layers 1 and 2 of Scheme 3 showed the largest decrease.

[0209] Based on the cumulative production, daily production stabilization time, decrease in remaining gas abundance, and decrease in pressure in the comprehensive study area, Scheme 3 was confirmed as the optimal development scheme for the original well network numerical model.

[0210] Step S04: On the pressure distribution map and residual gas abundance distribution map predicted by the optimal development scheme of the original well network numerical model, determine the future residual gas potential area, and then take the intersection with the static residual gas potential area to select the future residual gas sweet spot area.

[0211] In this invention, the optimal development scheme of the determined original well network numerical model is used to predict the pressure distribution and residual gas abundance distribution over a future period of time, and to select the future residual gas potential area. Then, the intersection of this selection with the static residual gas potential area is taken to obtain the future residual gas sweet spot area. The method includes the following steps:

[0212] Based on the optimal development scheme of the original well network numerical model, predict the pressure distribution and remaining gas abundance distribution for a period of time in the future;

[0213] Based on the optimal development scheme of the original well network numerical model, the area with the minimum pressure drop is identified on the pressure distribution map for a predicted period of time in the future.

[0214] Based on the optimal development scheme of the original well network numerical model, the region with the smallest decrease in remaining gas abundance is found on the distribution map of remaining gas abundance in the future period.

[0215] The intersection of the region with the minimum pressure drop and the region with the minimum decrease in remaining gas abundance is used to obtain the potential area of ​​future remaining gas.

[0216] By combining the future remaining gas potential area with the static remaining gas potential area, the future remaining gas sweet spot area is obtained.

[0217] In this embodiment of the invention, the pressure distribution and residual gas abundance distribution of the study area 20 years later are predicted using Scheme 3. The pressure in the study area decreases from 35.04 MPa in 2024 to 27.79 MPa in 2044, an average decrease of 17.8%. The area with the smallest pressure decrease is mainly concentrated between P1 and CXP3 in the northern part of the study area (e.g., Figure 6 (As shown in a). The residual gas abundance in Layer 1 of the study area decreased from 1.1544 in 2024 to 0.8986 in 2044, an average decrease of 22.2%. The areas with the smallest decrease in residual gas abundance in Layer 1 were mainly concentrated in the northern part of the study area between P1 and CXP3 (e.g., Figure 6 (As shown in b). The residual gas abundance in Layer 2 of the study area decreased from 0.3604 in 2024 to 0.3185 in 2044, an average decrease of 11.6%. The area with the smallest decrease in residual gas abundance in Layer 2 was near 6-1 in the northern part of the study area (e.g., Figure 6 (as shown in c).

[0218] By intersecting the region with the smallest pressure drop with the region with the smallest decrease in residual gas abundance in layer 1, three potential residual gas regions in layer 1 are obtained: Potential residual gas region 1 has a residual gas abundance of 3.55, potential residual gas region 2 has a residual gas abundance of 2.95, and potential residual gas region 3 has a residual gas abundance of 4.15 (e.g., ...). Figure 6 (As shown in b). The intersection of the region with the smallest pressure drop and the region with the smallest decrease in residual gas abundance in layer 2 yields one potential region for future residual gas in layer 2. To correspond with layer 1, this potential region for future residual gas in layer 2 is named Potential Region for Future Residual Gas 2, and its residual gas abundance is 3.25 (e.g., ...). Figure 6 (as shown in c).

[0219] By intersecting the future remaining gas potential areas of Layer 1 and Layer 2 with the static remaining gas potential areas, three future remaining gas sweet spots are obtained (e.g., Figure 7 (as shown in a to c).

[0220] Step S05: Take the intersection of the preferred current remaining gas dessert area and the preferred future remaining gas dessert area to determine the final remaining gas dessert area;

[0221] In this embodiment of the invention, the remaining dessert area (such as...) Figure 4 (as shown in a~c) and the future residual gas dessert area (such as...) Figure 7 Taking the intersection of (as shown in a~c), we obtain the final three remaining dessert areas (as shown in the diagram). Figure 8 (as shown in a to c).

[0222] Step S06: Deploy a virtual well in the final remaining gas sweet spot area to verify the final remaining gas sweet spot area.

[0223] In this invention, a virtual well is deployed in the obtained final residual gas sweet spot area to verify the final residual gas sweet spot area. The method includes the following steps:

[0224] Obtain virtual well parameter settings; the virtual well parameters include: perforation interval, fracturing interval, single well permeability, conductivity coefficient, and reasonable production data;

[0225] After deploying virtual wells, a new well network digital model is formed. Through an effective reservoir model, reference wells for the virtual wells are found, and data on the perforation interval, fracturing interval, single-well permeability, conductivity coefficient, and reasonable production rate of the virtual wells are determined.

[0226] Based on the effective reservoir model profiles of the virtual well and the reference well, we find the effective reservoir development intervals, perform perforation and fracturing, and set reasonable production data for the virtual well.

[0227] Based on the reference well, set the single-well permeability and conductivity coefficient of the virtual well;

[0228] After the virtual well parameters are set, the development scheme of the new well network digital model is optimized on the new well network digital model to obtain the optimal development scheme of the new well network digital model. Based on the obtained optimal development scheme of the new well network digital model, the pressure distribution map and the residual gas abundance distribution map for a predicted future period are obtained. If the pressure and residual gas abundance of the new well network digital model at the virtual well are significantly lower than those of the original well network digital model, the final residual gas sweet spot is verified.

[0229] In this invention, the optimal development scheme for the new well network numerical model is selected based on the new well network numerical model to obtain the optimal development scheme for the new well network numerical model. The method includes the following steps:

[0230] The new well network digital model includes several development schemes;

[0231] For all the above-mentioned new well network numerical model development schemes, the development scheme prediction for a future period of time is carried out. The cumulative production, daily production, pressure distribution map and remaining gas abundance map of the study area are compared and the development scheme with the highest cumulative production, the longest stable daily production time, and the largest pressure drop and remaining gas abundance drop in the study area is determined as the best development scheme for the new well network numerical model.

[0232] If the four parameters of maximum cumulative production, longest stable daily production, greatest pressure drop, and greatest decrease in remaining gas abundance cannot occur simultaneously, then the optimal development scheme for the new well network numerical model will be determined by prioritizing maximum cumulative production as the first factor, greatest decrease in remaining gas abundance as the second factor, longest stable daily production as the third factor, and greatest pressure drop as the fourth factor.

[0233] In this embodiment of the invention, well P6 is deployed in sweet spot zone 1 of layer 1, well P5 in sweet spot zone 3, and well 6-5 in sweet spot zone 2, forming a new well network numerical model of 14 wells. Well P6 is in the same well site as well CXP3, well P5 is in the same well site as well P1, and well 6-5 is close to well 6-1. Therefore, the perforation interval, fracturing interval, single-well permeability, conductivity coefficient, and reasonable production data of well P6 are all referenced to well CXP3; well P5 is referenced to well P1; and well 6-5 is referenced to well 6-1.

[0234] In this embodiment of the invention, based on Scheme 3 and combined with the daily production data obtained from virtual wells, a new well network numerical model development scheme is formulated, including Schemes 4, 5, and 6. Based on the new well network numerical model, scheme predictions are performed, with the prediction period from June 2024 to June 2044, and a reporting frequency of once a year, conducting 20-year scheme predictions. The new well network numerical model scheme predictions are shown in Table 3.

[0235] Table 3:

[0236]

[0237] Based on single-well predictions, two wells in the study area will have higher cumulative gas production under Scheme 4 and two wells in Scheme 6.

[0238] Based on the cumulative production forecast for the study area, the cumulative production of Scheme 4 is 2.69 billion cubic meters, Scheme 5 is 2.81 billion cubic meters, and Scheme 6 is 2.88 billion cubic meters. Scheme 6 has the highest cumulative production, exceeding Scheme 3 by 470 million cubic meters (e.g., ...). Figure 9 (as shown in a).

[0239] Based on the predicted daily output stabilization period for the study area, the stabilization period for daily output under Scheme 4 is 4 years, under Scheme 5 is 2 years, and under Scheme 6 is 1 year. Scheme 4 has the longest stabilization period (e.g., ...). Figure 9 (as shown in b).

[0240] Based on the predicted pressure reduction in the study area, the pressure under Scenario 4 will decrease from 35.04 MPa in 2024 to 26.09 MPa in 2044, an average decrease of 19.8%; under Scenario 5, the pressure will decrease from 35.04 MPa in 2024 to 25.83 MPa in 2044, an average decrease of 20.6%; and under Scenario 6, the pressure will decrease from 35.04 MPa in 2024 to 25.68 MPa in 2044, an average decrease of 21.0%. Scenario 6 represents the largest pressure reduction (e.g., ...). Figure 9 c. Figure 10 (as shown in a).

[0241] Based on the predicted residual gas abundance in the study area, the residual gas abundance in Layer 1 under Scheme 4 is projected to decrease from 1.1544 in 2024 to 0.8571 in 2044, an average decrease of 25.7%; under Scheme 5, the residual gas abundance in Layer 1 is projected to decrease from 1.1544 in 2024 to 0.8569 in 2044, an average decrease of 25.8%; and under Scheme 6, the residual gas abundance in Layer 1 is projected to decrease from 1.1544 in 2024 to 0.8313 in 2044, an average decrease of 27.9% (e.g., ...). Figure 10 (As shown in b); In Scheme 4 of the study area, the residual gas abundance in Layer 2 decreases from 0.3604 in 2024 to 0.3145 in 2044, with an average decrease of 12.7%; In Scheme 5 of the study area, the residual gas abundance in Layer 2 decreases from 0.3604 in 2024 to 0.3139 in 2044, with an average decrease of 12.9%; In Scheme 6 of the study area, the residual gas abundance in Layer 2 decreases from 0.3604 in 2024 to 0.3111 in 2044, with an average decrease of 13.7% (as shown in b); Figure 10 As shown in c), the residual gas abundance in layers 1 and 2 of scheme 6 in the study area decreased the most.

[0242] Based on the cumulative production, daily production stabilization time, decrease in remaining gas abundance, and decrease in pressure in the comprehensive study area, Scheme 6 was confirmed as the optimal development scheme for the new well network numerical model.

[0243] In this embodiment of the invention, the pressure changes at well points in scheme four of the study area are as follows: the pressure at well P6 decreased from 34.1 MPa in 2024 to 26.4 MPa in 2044, a decrease of 22.5%; the pressure at well 6-5 decreased from 34.5 MPa in 2024 to 33.2 MPa in 2044, a decrease of 3.8%; the pressure at well P5 decreased from 33.7 MPa in 2024 to 23.2 MPa in 2044, a decrease of 31.1%; the average pressure of the three wells decreased by 19.2%. Changes in residual gas abundance at well point 4 in the study area: The residual gas abundance at well P6 decreased from 3.51 in 2024 to 1.92 in 2044, a decrease of 45.2%; the residual gas abundance at well 6-5 decreased from 2.24 in 2024 to 1.95 in 2044, a decrease of 12.9%; the residual gas abundance at well P5 decreased from 4.15 in 2024 to 2.93 in 2044, a decrease of 29.3%; the average residual gas abundance of the three wells decreased by 29.2%. Changes in residual gas abundance at well point 4 in the study area: The residual gas abundance at well P6 decreased from 0.72 in 2024 to 0.31 in 2044, a decrease of 56.4%; the residual gas abundance at well 6-5 decreased from 2.48 in 2024 to 2.34 in 2044, a decrease of 5.6%; the residual gas abundance at well P5 decreased from 0.46 in 2024 to 0.33 in 2044, a decrease of 28.2%; the average residual gas abundance of the three wells decreased by 30.1%.

[0244] Pressure changes at well points in Scheme 5 of the study area: The pressure at well P6 decreased from 34.1 MPa in 2024 to 25.5 MPa in 2044, a decrease of 25.2%; the pressure at well 6-5 decreased from 34.5 MPa in 2024 to 32.2 MPa in 2044, a decrease of 6.7%; the pressure at well P5 decreased from 33.7 MPa in 2024 to 21.9 MPa in 2044, a decrease of 35.0%; the average pressure of the three wells decreased by 22.3%. Changes in residual gas abundance at well point 5 in the study area: The residual gas abundance at well P6 decreased from 3.51 in 2024 to 1.15 in 2044, a decrease of 67.2%; the residual gas abundance at well 6-5 decreased from 2.24 in 2024 to 2.1 in 2044, a decrease of 6.3%; the residual gas abundance at well P5 decreased from 4.15 in 2024 to 2.9 in 2044, a decrease of 30.1%; the average residual gas abundance of the three wells decreased by 34.5%. Changes in residual gas abundance at well point 5 in the study area: The residual gas abundance at well P6 decreased from 0.72 in 2024 to 0.29 in 2044, a decrease of 59.5%; the residual gas abundance at well 6-5 decreased from 2.48 in 2024 to 2.21 in 2044, a decrease of 10.1%; the residual gas abundance at well P5 decreased from 0.46 in 2024 to 0.28 in 2044, a decrease of 39.1%; the average residual gas abundance of the three wells decreased by 31.5%.

[0245] Pressure changes at well points in the study area (Scheme 6): The pressure at well P6 decreased from 34.1 MPa in 2024 to 25.1 MPa in 2044, a decrease of 26.4%; the pressure at well 6-5 decreased from 34.5 MPa in 2024 to 29.1 MPa in 2044, a decrease of 15.6%; the pressure at well P5 decreased from 33.7 MPa in 2024 to 19.1 MPa in 2044, a decrease of 43.3%; the average pressure decrease across the three wells was 28.4% (e.g., ...). Figure 10 (As shown in a). Changes in residual gas abundance at well point 6 in the study area: The residual gas abundance at well P6 decreased from 3.51 in 2024 to 1.47 in 2044, a decrease of 58.1%; the residual gas abundance at well 6-5 decreased from 2.24 in 2024 to 1.9 in 2044, a decrease of 15.1%; the residual gas abundance at well P5 decreased from 4.15 in 2024 to 2.2 in 2044, a decrease of 46.9%; the average residual gas abundance of the three wells decreased by 40.1% (e.g., ...). Figure 10(As shown in b). Changes in residual gas abundance at well point 6 in the study area: The residual gas abundance at well P6 decreased from 0.72 in 2024 to 0.31 in 2044, a decrease of 56.7%; the residual gas abundance at well 6-5 decreased from 2.48 in 2024 to 2.31 in 2044, a decrease of 6.8%; the residual gas abundance at well P5 decreased from 0.46 in 2024 to 0.3 in 2044, a decrease of 34.7%; the average residual gas abundance of the three wells decreased by 32.7% (e.g., ...). Figure 10 (as shown in c).

[0246] Based on the comprehensive analysis of the decrease in average pressure, decrease in residual gas abundance in the sub-layers, and decrease in average pressure and residual gas abundance at the virtual well points, the pressure and residual gas abundance of the new well network numerical model at the virtual well point in Scheme 6 of the study area decreased most significantly compared with the pressure and residual gas abundance of the original well network numerical model, thus verifying the final residual gas sweet spot area.

[0247] It is understood that the various method embodiments mentioned above in this invention can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this invention will not elaborate further.

[0248] The method for evaluating the remaining sweet spot of gas in tight sandstone and conglomerate gas reservoirs can be executed by a device for evaluating the remaining sweet spot of gas in tight sandstone and conglomerate gas reservoirs. For example, the method can be executed by a terminal device, a server, or other processing device. The terminal device can be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, wearable device, etc. In some possible implementations, the method for evaluating the remaining sweet spot of gas in tight sandstone and conglomerate gas reservoirs can be implemented by a processor calling computer-readable instructions stored in memory.

[0249] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0250] In this invention, an apparatus for evaluating the residual gas sweet spot region of tight sandstone and conglomerate gas reservoirs is also proposed, comprising:

[0251] The first optimization unit is used to obtain the original well network fine geological model and the original well network numerical model of the study area. Based on the original well network fine geological model, the static residual gas potential area is optimized by using the structural model, sedimentary facies model and reservoir model.

[0252] The second optimization unit is used to determine the current remaining gas potential area based on the optimization results of the static remaining gas potential area, using the current pressure distribution map and remaining gas abundance distribution map of the original well network numerical model, and then take the intersection with the static remaining gas potential area to optimize the current remaining gas sweet spot area.

[0253] The first determining unit is used to predict the development scheme based on the original well network numerical model, and to determine the optimal development scheme of the original well network numerical model by combining the cumulative production map, daily production map, pressure distribution map and remaining gas abundance distribution map.

[0254] The third optimization unit is used to determine the future residual gas potential area on the pressure distribution map and residual gas abundance distribution map predicted by the optimal development scheme of the original well network numerical model, and then take the intersection with the static residual gas potential area to optimize the future residual gas sweet spot area.

[0255] The second determining unit is used to take the intersection of the preferred current remaining gas dessert area and the preferred future remaining gas dessert area to determine the final remaining gas dessert area.

[0256] A verification unit is used to deploy a virtual well in the final remaining gas sweet spot area to verify the final remaining gas sweet spot area.

[0257] In some embodiments, the functions or modules and units included in the apparatus provided by the present invention can be used to execute the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0258] This invention innovatively utilizes a synergistic technique involving structural, sedimentary facies, and reservoir models to optimize static residual gas potential zones based on the original well network's refined geological model. It innovatively uses a comparison technique between the current pressure and current residual gas abundance of the original well network's numerical model to determine current residual gas potential zones, and then takes the intersection with the static residual gas potential zones to optimize current residual gas sweet spots. Innovatively, based on the original well network's numerical model, it employs techniques of determining total cumulative production, daily production within a fixed timeframe, pressure variation, and abundance potential to determine the optimal development scheme for the original well network's numerical model. Innovatively, it uses the original well network's numerical model's optimal development scheme prediction technique, combined with future pressure and future residual gas abundance changes, to determine future residual gas potential zones, and then takes the intersection with the static residual gas potential zones to optimize future residual gas sweet spots. Innovatively, it employs a technique to integrate current and future residual gas sweet spots to determine the final residual gas sweet spot. Innovatively, it deploys virtual wells in the final residual gas sweet spot and verifies the final residual gas sweet spot using a study area and virtual well point verification technique.

[0259] This invention enables systematic, accurate, and rapid optimization of residual gas sweet spots in tight sandstone and conglomerate gas reservoirs. It can be used for well site deployment during the development phase, reducing well placement risks, increasing the success rate of drilling into industrial gas flow wells in development wells, and achieving high-efficiency and sustainable development of development wells.

[0260] Using the method of this invention, three final remaining gas sweet spots were found in the CD gas field, a tight sandstone and conglomerate gas reservoir. Well P6 was deployed in sweet spot 1, well 6-5 in sweet spot 2, and well P5 in sweet spot 3. All three wells have been completed and have yielded industrial gas flow, achieving a 100% success rate in encountering industrial gas flow wells. The well-controlled reserves of these three wells are 1.5 billion cubic meters, bringing the total well-controlled reserves of the CD gas field to 6.4 billion cubic meters, representing 91% of the geological reserves. This demonstrates the high-efficiency and sustainable development of the tight sandstone and conglomerate gas reservoir.

[0261] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for evaluating the residual gas sweet spot region of tight sandstone and conglomerate gas reservoirs, characterized in that, include: We obtained a detailed geological model and a numerical model of the original well network in the study area. Based on the detailed geological model of the original well network, we used structural models, sedimentary facies models, and reservoir models to select the static residual gas potential areas. Based on the results of the static residual gas potential area selection, the current pressure distribution map and residual gas abundance distribution map of the original well network numerical model are used to determine the current residual gas potential area, and then the intersection with the static residual gas potential area is taken to select the current residual gas sweet spot area. Based on the original well network numerical model, development scheme prediction is performed. Combining the cumulative production map, daily production map, pressure distribution map and remaining gas abundance distribution map, the optimal development scheme of the original well network numerical model is determined. Based on the pressure distribution map and residual gas abundance distribution map predicted by the optimal development scheme of the original well network numerical model, the future residual gas potential area is determined, and then the intersection with the static residual gas potential area is taken to select the future residual gas sweet spot area. The final remaining gas dessert area is determined by taking the intersection of the preferred current remaining gas dessert area and the preferred future remaining gas dessert area. A virtual well is deployed in the final remaining gas sweet spot area to verify the final remaining gas sweet spot area.

2. The method for evaluating the residual gas sweet spot region of a tight sandstone-conglomerate gas reservoir according to claim 1, characterized in that, Before obtaining the detailed geological model of the original well network, the detailed geological model of the original well network is established. The method includes the following steps: The original well network's detailed geological model includes: a structural model, a sedimentary facies model, and a reservoir model; Obtain the structural model of the study area; The construction model includes a plane model and a fault model; Based on seismic interpretation horizon data, seismic velocity volume, and well point stratification data, the time-domain seismic interpretation horizon data is converted into depth-domain seismic interpretation horizon data through seismic velocity volume. The depth-domain seismic interpretation horizon data is then corrected using well point stratification data, and a horizon model is established by combining well and seismic data. Based on seismic fault data, seismic velocity volume, and wellpoint fault data, the time-domain seismic fault data is converted into depth-domain seismic fault data through seismic velocity volume, and the depth-domain seismic fault data is corrected with wellpoint fault data. A fault model is established by combining well and seismic data. Obtain sedimentary facies models for the study area; A sedimentary facies model is established by using a region assignment method on the digitized sedimentary facies map; Obtain reservoir models for the study area; The reservoir model includes a favorable reservoir model and an effective reservoir model; The favorable reservoir refers to a reservoir that includes a gas layer, a gas-diffusion layer, a gas-diffusion boundary layer, and a water layer. The effective reservoir refers to a reservoir that includes a gas layer, a gas-differential layer, and a gas-differential boundary layer. Well logging interpretation results include gas layers, differential gas layers, differential gas boundary layers, and water layers. Based on the well logging interpretation results, well points are mainly based on the interpretation results, and sedimentary facies control is used between wells. Combined with Gaussian stochastic simulation algorithm, interpolation is performed to establish favorable reservoir models. Then, the favorable reservoir models are used as synergistic conditions to establish effective reservoir models.

3. The method for evaluating the residual gas sweet spot region of tight sandstone and conglomerate gas reservoirs according to claim 2, characterized in that, The method for selecting static residual gas potential zones using the aforementioned structural model, sedimentary facies model, and reservoir model includes the following steps: The static residual gas potential zone was selected by taking the simultaneous fulfillment of three criteria: the structure is 60m above the gas-water interface, the sedimentary microfacies type is distributary channel facies, and the reservoir type is better than the gas boundary layer. If the three conditions of the structure being 60m above the gas-water interface, the sedimentary microfacies being distributary channel facies, and the reservoir type being superior to the gas boundary layer cannot be met simultaneously, then the reservoir type being superior to the gas boundary layer is taken as the first factor, the sedimentary microfacies being distributary channel facies as the second factor, and the structure being 60m above the gas-water interface as the third factor, and the static residual gas potential zone is determined by prioritizing these factors in that order.

4. The method for evaluating the residual gas sweet spot region of a tight sandstone-conglomerate gas reservoir according to claim 1, characterized in that, Before obtaining the original well network numerical model, the original well network numerical model is built and historical fitting is performed.

5. The method for evaluating the residual gas sweet spot region of a tight sandstone-conglomerate gas reservoir according to claim 4, characterized in that, Before building the original well network digital model, the original well network fine geological model is coarsened to obtain the original well network coarsened geological model. The method includes the following steps: A fine geological model of the original well network in the study area is obtained. Based on the results of the fine geological model of the original well network, a coarse geological model of the original well network is generated by coarsening the grid. Based on the three parameters of non-orthogonality of the grid, negative volume of the grid, and grid distortion, the quality control of the original well grid coarsening geological model is carried out to ensure that the original well grid coarsening geological model meets the requirements for establishing the original well grid numerical model.

6. The method for evaluating the residual gas sweet spot in tight sandstone and conglomerate gas reservoirs according to claim 5, characterized in that, The method for quality control of the coarsened geological model of the original well network includes the following steps: The term "non-orthogonal grid" refers to the degree to which the grid angle deviates from 90°. When its absolute value is less than 30°, it indicates that the grid orthogonality is relatively good. The negative volume of the grid indicates whether the grid volume is positive or negative. When the grid volume is greater than 0, it means that the grid has no negative volume. The mesh distortion deformation represents the magnitude of the mesh distortion change. When the mesh distortion deformation is near 0, it indicates that there is no mesh distortion deformation.

7. The method for evaluating the residual gas sweet spot in tight sandstone and conglomerate gas reservoirs according to claim 5, characterized in that, After coarsening the original well network's fine geological model, dynamic data is loaded to obtain dynamic data for the target layer; the dynamic data includes: production data, measures data, and rock fluid parameters; The splitting coefficient is determined based on the ratio of the formation coefficient of each gas-producing segment to the formation coefficient of the total gas-producing segments. Combined with the gas production at the wellhead, the production of the target segment at the wellhead is split to obtain the production data of the target segment. The formation coefficient = effective thickness × permeability. The data on the measures include data on perforation, perforation repair, fracturing, and acidizing. Based on the implementation records of measures at the target level, obtain the measure data for the target level; The rock fluid parameters include PVT parameters, rock compressibility coefficient, and relative permeability curve parameter data; Based on high-pressure physical property tests and relative permeability tests of the core samples from the target layer, PVT parameters, rock compressibility coefficient, and relative permeability curve parameters of the target layer were obtained.

8. The method for evaluating the residual gas sweet spot in tight sandstone and conglomerate gas reservoirs according to claim 4, characterized in that, The method for constructing the original well network digital model includes the following steps: Selection of original well network numerical model type; The initialization conditions for the original well network numerical model have been determined; Development strategy for the original well network digital model.

9. The method for evaluating the residual gas sweet spot in tight sandstone and conglomerate gas reservoirs according to claim 8, characterized in that, The type of original well network numerical model is selected as follows: Based on the fact that the study area is a tight sandstone-conglomerate gas reservoir, which is a normal pressure gas reservoir with stable internal components, the model type was selected, and the result was a black oil model with three-phase flow of oil, gas and water.

10. The method for evaluating the residual gas sweet spot region of a tight sandstone-conglomerate gas reservoir according to claim 8, characterized in that, The initialization conditions of the original well network numerical model include the balanced method and the unbalanced enumeration method; The equilibrium method refers to a situation where the air-water interface is composed of air above and water below, the initial state of the air and water is stable and does not flow, and the initial water saturation of the model is calculated according to the bound water saturation value of the relative permeability curve. The non-equilibrium enumeration method refers to the initial unstable state of air and water, where flow occurs, and the initial water saturation of the model is calculated according to the given value, rather than according to the bound water saturation value of the relative permeability curve. Based on the fact that gas reservoirs are initially stable and do not flow after formation, the equilibrium method is determined as the initialization condition.

11. The method for evaluating residual gas sweet spots in tight sandstone and conglomerate gas reservoirs according to claim 8, characterized in that, The original well network digital model development strategy includes pressure-controlled production reduction and production-controlled pressure reduction; The so-called constant pressure reduction refers to reducing output to maintain constant pressure; The term "fixed production pressure reduction" refers to maintaining a constant output by reducing pressure. Based on the needs of gas reservoir development, formulate a development strategy of fixed production and pressure reduction.

12. The method for evaluating the residual gas sweet spot region of a tight sandstone-conglomerate gas reservoir according to claim 4, characterized in that, The historical fitting of the original well network numerical model includes reserve fitting, production fitting, and pressure fitting.

13. The method for evaluating residual gas sweet spots in tight sandstone and conglomerate gas reservoirs according to claim 12, characterized in that, The aforementioned reserve fitting refers to the fitting of the reserves in the original well network numerical model after coarsening with the reserves in the original well network fine geological model before coarsening. Based on the fitting results, by adjusting the net-to-gross ratio parameter, the reserves of the original well network numerical model after coarsening are compared with the reserves of the original well network fine geological model before coarsening, so that the reserves of the original well network numerical model after coarsening meet the error requirements and the reserves are fitted. The production fitting includes production fitting for the study area and production fitting for individual wells; Based on the comparative analysis of the simulation curves of the original well network numerical model of cumulative production and daily production in the study area and the actual production data curves, the permeability of each zone is adjusted to make the simulation curves of the original well network numerical model of cumulative production and daily production in the study area fit the actual production data curves. Based on the comparative analysis of the simulation curves of the original well network numerical model of cumulative production and daily production of single wells and the actual production data curves, the permeability and conductivity of single wells are adjusted to make the simulation curves of the original well network numerical model of cumulative production and daily production of single wells fit the actual production data curves. The pressure fitting refers to the fitting of the simulated pressure of the original well network numerical model of a single well with the actual monitored pressure of the single well. Based on the comparative analysis of the simulated pressure curves of the original well network digital model and the actual monitored pressure curves of the single well, the permeability and conductivity of the single well are adjusted to fit the simulated pressure curves of the original well network digital model with the actual monitored pressure curves of the single well.

14. The method for evaluating the residual gas sweet spot region of a tight sandstone-conglomerate gas reservoir according to claim 1, characterized in that, Based on the optimization results of the static remaining gas potential area, the current remaining gas potential area is determined, and then the intersection with the static remaining gas potential area is taken to optimize the current remaining gas sweet spot area. The specific method includes the following steps: Based on the current pressure distribution map and remaining gas abundance distribution map of the original well network numerical model, the current remaining gas potential area is selected. On the pressure distribution map of the original well network numerical model, find the region with the minimum pressure drop; On the residual gas abundance distribution map of the original well network numerical model, find the region with the minimum decrease in residual gas abundance; The intersection of the region with the minimum pressure drop and the region with the minimum decrease in remaining gas abundance is used to obtain the current remaining gas potential area. By combining the current remaining gas potential area with the static remaining gas potential area, the current remaining gas sweet spot area is obtained.

15. The method for evaluating the residual gas sweet spot region of a tight sandstone-conglomerate gas reservoir according to claim 1, characterized in that, Based on the original well network numerical model, development scheme prediction is performed. Combining the cumulative production map, daily production map, pressure distribution map, and remaining gas abundance distribution map, the optimal development scheme of the original well network numerical model is determined. The method includes the following steps: The original well network numerical model includes several development schemes; For all the above-mentioned original well network numerical model development schemes, the development schemes for the future period are predicted. The cumulative production, daily production, pressure distribution map and remaining gas abundance map of the study area are compared. The development scheme with the highest cumulative production, the longest stable daily production time, and the largest decrease in pressure and remaining gas abundance in the study area is determined as the best development scheme of the original well network numerical model. If the four parameters of maximum cumulative production, longest stable daily production, greatest pressure drop, and greatest decrease in remaining gas abundance cannot occur simultaneously, then the optimal development scheme of the original well network numerical model will be determined by prioritizing maximum cumulative production, the second factor, the third factor, the third factor, and the fourth factor, in that order.

16. The method for evaluating the residual gas sweet spot region of a tight sandstone-conglomerate gas reservoir according to claim 15, characterized in that, Based on the optimal development scheme of the original well network numerical model, the pressure distribution and residual gas abundance distribution in the future are predicted, the future residual gas potential area is selected, and then the intersection with the static residual gas potential area is taken to obtain the future residual gas sweet spot area. The method includes the following steps: Based on the optimal development scheme of the original well network numerical model, predict the pressure distribution and remaining gas abundance distribution for a period of time in the future; Based on the optimal development scheme of the original well network numerical model, the area with the minimum pressure drop is identified on the pressure distribution map for a predicted period of time in the future. Based on the optimal development scheme of the original well network numerical model, the region with the smallest decrease in remaining gas abundance is found on the distribution map of remaining gas abundance in the future period. The intersection of the region with the minimum pressure drop and the region with the minimum decrease in remaining gas abundance is used to obtain the potential area of ​​future remaining gas. By combining the future remaining gas potential area with the static remaining gas potential area, the future remaining gas sweet spot area is obtained.

17. The method for evaluating the residual gas sweet spot region of a tight sandstone-conglomerate gas reservoir according to claim 1, characterized in that, Deploying a virtual well in the obtained final remaining gas sweet spot area and verifying the final remaining gas sweet spot area includes the following steps: Obtain virtual well parameter settings; The virtual well parameter settings include: perforation interval, fracturing interval, single well permeability, conductivity coefficient, and reasonable production data; After deploying virtual wells, a new well network digital model is formed. Through an effective reservoir model, reference wells for the virtual wells are found, and data on the perforation interval, fracturing interval, single-well permeability, conductivity coefficient, and reasonable production rate of the virtual wells are determined. Based on the effective reservoir model profiles of the virtual well and the reference well, we find the effective reservoir development intervals, perform perforation and fracturing, and set reasonable production data for the virtual well. Based on the reference well, set the single-well permeability and conductivity coefficient of the virtual well; After the virtual well parameters are set, the optimal development scheme for the new well network digital model is selected on the new well network digital model to obtain the best development scheme for the new well network digital model. Based on the optimal development scheme of the new well network numerical model, the pressure distribution map and the remaining gas abundance distribution map for a predicted future period are obtained; if the pressure and remaining gas abundance of the new well network numerical model at the virtual well are significantly lower than those of the original well network numerical model, the final remaining gas sweet spot is verified.

18. The method for evaluating the residual gas sweet spot in tight sandstone and conglomerate gas reservoirs according to claim 17, characterized in that, Based on the new well network numerical model, the optimal development scheme for the new well network numerical model is selected. The method includes the following steps: The new well network digital model includes several development schemes; For all the above-mentioned new well network numerical model development schemes, the development scheme prediction for a future period of time is carried out. The cumulative production, daily production, pressure distribution map and remaining gas abundance map of the study area are compared and the development scheme with the highest cumulative production, the longest stable daily production time, and the largest pressure drop and remaining gas abundance drop in the study area is determined as the best development scheme for the new well network numerical model. If the four parameters of maximum cumulative production, longest stable daily production, greatest pressure drop, and greatest decrease in remaining gas abundance cannot occur simultaneously, then the optimal development scheme for the new well network numerical model will be determined by prioritizing maximum cumulative production as the first factor, greatest decrease in remaining gas abundance as the second factor, longest stable daily production as the third factor, and greatest pressure drop as the fourth factor.

19. The method for evaluating residual gas sweet spots in tight sandstone and conglomerate gas reservoirs according to claim 16 or 18, further characterized in that: The "future period" refers to 20 years from now.

20. An apparatus for evaluating the sweet spot region of residual gas in tight sandstone and conglomerate gas reservoirs, characterized in that, include: The first optimization unit is used to obtain the original well network fine geological model and the original well network numerical model of the study area. Based on the original well network fine geological model, the static residual gas potential area is optimized by using the structural model, sedimentary facies model and reservoir model. The second optimization unit is used to determine the current remaining gas potential area based on the optimization results of the static remaining gas potential area, using the current pressure distribution map and remaining gas abundance distribution map of the original well network numerical model, and then take the intersection with the static remaining gas potential area to optimize the current remaining gas sweet spot area. The first determining unit is used to predict the development scheme based on the original well network numerical model, and to determine the optimal development scheme of the original well network numerical model by combining the cumulative production map, daily production map, pressure distribution map and remaining gas abundance distribution map. The third optimization unit is used to determine the future residual gas potential area on the pressure distribution map and residual gas abundance distribution map predicted by the optimal development scheme of the original well network numerical model, and then take the intersection with the static residual gas potential area to optimize the future residual gas sweet spot area. The second determining unit is used to take the intersection of the preferred current remaining gas dessert area and the preferred future remaining gas dessert area to determine the final remaining gas dessert area. A verification unit is used to deploy a virtual well in the final remaining gas sweet spot area to verify the final remaining gas sweet spot area.

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